The effect of quantum computing on Artificial Intelligence could be as understated as it is profound.

Some say quantum computing is necessary to achieve General Artificial Intelligence. Certain expressions of this paradigm, such as quantum annealing, are inherently probabilistic and optimal for machine learning. The most pervasive quantum annealing use cases center on optimization and constraints, problems that have traditionally involved non-statistical AI approaches like rules, symbols, and reasoning.

When one considers the fact that there are now cloud options for accessing this form of quantum computing (replete with resources for making it enterprise-applicable for any number of deployments) sans expensive hardware, one fact becomes unmistakably clear.

With quantum computing, a lot of times were talking about what will it be able to do in the future, observed Mark Johnson,D-WaveSVP of Quantum Technologies and Systems Products. But no, you can do things with it today.

Granted, not all those things involve data science intricacies. Supply chain management and logistics are just as easily handled by quantum annealing technologies. But, when these applications are considered in tandem with some of the more progressive approaches to AI-enabled by quantum annealing, their esteem to organizations across verticals becomes apparent.

Quantum annealing involves the variety of quantum computing in which, when the quantum computer reaches its lowest energy state, it solves a specific problem even NP-hard problems. Thus, whether users are trying to select features for a machine learning model or the optimum route to send a fleet of grocery store delivery drivers, quantum annealing approaches provide these solutions when the lowest energy state is achieved. Annealing quantum computing is a heuristic probabilistic solver, Johnson remarked. So, you might end up with the very best answer possible or, if you dont, you will end up with a very good answer.

Quantum annealings merit lies in its ability to supply these answers at an enormous scale such as that required for a defense agencys need to analyze all possible threats and responses for a specific location at a given time. It excels in cases in which you need to consider many, many possibilities and its hard to wade through them, Johnson mentioned. Classical computational models consider each possibility one at a time for such a combinatorial optimization problem.

Quantum annealing considers those possibilities simultaneously.

The data science implications for this computational approach are almost limitless. One developer resource D-Wave has made available via the cloud is a plug-in for the SDK for Ocean a suite of open source Python tools that integrates with scikit-learn to improve feature selection. It supports recognizing in a large pattern of data, can I pick out features that correlate with certain things and being able to navigate that, Johnson remarked. I understand it ends up mapping into an optimization problem. The statistical aspects of quantum annealing are suitable for other facets of advanced machine learning, too.

According to Johnson, because of its probabilistic nature, one of the interesting things that quantum annealing does is not just picking the best answer or a good answer, but coming up with a distribution, a diversity of answers, and understanding the collection of answers and a little about how they relate to each other. This quality of quantum annealing is useful for numerous dimensions of machine learning includingbackpropagation, which is used to adjust a neural networks parameters while going from the output to the input. It can also reinforce what Johnson termed Boltzmann sampling, which involves randomly sampling combinatorial structures.

There are considerable advantages to making quantum annealing available through the cloud. The cloud architecture for accessing this form of computing is just as viable for accessing what Johnson called the gate model type of quantum computing, which is primed for factoring numbers and used in RSA encryption schema, Johnson confirmed. Organizations can avail themselves of both quantum computing methods in D-Waves cloud platform. Moreover, they can also utilize hybrid quantum and classical computinginfrastructure as well, which is becoming ever more relevant in modern quantum computing conversations. You would just basically be using both of them together for the part of the problem thats most efficient, Johnson explained.

In addition to the ready availability of each of these computational models, D-Waves cloud platform furnishes documentation for a range of example use cases for common business problems across industries. Theres also an integrated developer environment you can pull up that already has in it Ocean, our open source suite of tools, which help the developer interface with the quantum computer, Johnson added. Examples include the ability to write code in Python. When organizations find documentation in the cloud about a previous use case thats similar to theirs, You can pull up sample code that will use the quantum computer to solve that problem in your integrated developer environment, Johnson noted.

That sample code provides an excellent starting point for developers to build applications for applying quantum computing and hybrid quantum and classical computing methods to an array of business problems pertaining to financial services, manufacturing, life sciences, manufacturing, and more. Its just one of the many benefits of quantum computing through the cloud. The appeal of quantum annealing, of course, lies in its ability to expedite the time required to solve combinatorial optimization problems.

As the ready examples of quantum solutions the vast majority of which entail quantum annealing across the aforesaid verticals indicate, such issues are, the harder we look, ubiquitous throughout business, Johnson indicated. The data science utility of quantum annealing for feature selection, Boltzmann sampling, and backpropagation is equally horizontal and may prove influential to the adoption rates of this computational approach.

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D-Wave Suggests Quantum Annealing Could Help AI - The New Stack

Quantum computing stocks are having a moment. The industrys leading pure play company, IonQ (NYSE:IONQ), up nearly 400% year to date (YTD), has plenty of positive chatter about their proprietary technology. However, traders should avoid these three other quantum stocks that dont have the same apparent potential.

Its important to realize that there are various ways to generate qubits and enable quantum computing. A pitfall is to value all these methods similarly. Yet, trapped ion approaches, used by IonQ and Honeywell (NYSE:HON), show the most promise. Others, such as superconducting and quantum annealing, are less proven to this point.

Avoid making any painful quantum computing mistakes and steer clear of these three riskier quantum picks.

Source: Bartlomiej K. Wroblewski / Shutterstock.com

Rigetti Computing (NASDAQ:RGTI) is a small, pure play quantum computing firm focused on using superconducting technology to produce its qubits.

Researchers have noted that there are high error rates with superconducting transmon qubits. Efforts are under way to reduce the error rate in order to make superconducting a more competitive way of achieving quantum computing. For now, investors have gravitated to alternatives, such as trapped ion systems, used by rivals such as IonQ.

Rigetti enjoyed a brief moment in the sun this summer thanks to the superconducting media cycle. However, skeptics quickly debunked reports of a breakthrough in room temperature-based superconducting systems. This should put an end to the recent enthusiasm in RGTI stock.

At the end of the day, Rigetti is a tiny firm with minimal revenues attempting to popularize so-called second-tier quantum computing systems. All of that makes it a highly risky bet today.

Source: vs148 / Shutterstock

Quantum computing and quantum technologies have started to make their impacts known in other industries. For example, Arqit Quantum (NASDAQ:ARQQ), is attempting to commercialize quantum encryption to deliver next-generation cybersecurity solutions.

On paper, this seems like a promising venture. The CEO, David Williams, has given investors an optimistic vision of the company, declaring in 2021 that Arqit could be Britains biggest ever tech scale-up.

However, another statement he made at the time has now backfired.

We dont need to raise any more money, ever, Williams stated when Arqits SPAC deal was closing.

You can probably guess what happened next. Thats right, Arqit slammed investors with an unexpected capital raise earlier this year, causing the stock to careen 44% lower in a single day.

Perhaps quantum computing will be vital in developing future cybersecurity technologies. With little sign of it yet, Arqit is generating a disappointing $2.6 million of revenues through the first half of its latest fiscal year.

Source: T. Schneider / Shutterstock

D-Wave Quantum(NYSE:QBTS) is a Canadian technology company attempting to commercialize its quantum computing systems. It has a fifth-generation quantum computer and offers quantum computing services on-demand, in addition to its Ocean open-source python tools ecosystem.

D-Wave uses a quantum annealing approach which it sees as optimal for solving energy-minimization problems such as optimization and sampling.

It should be noted, however, that most quantum computing firms have chosen other approaches, rather than quantum annealing.

In fact, it has little evidence of widespread customer interest. D-Waves Q2 earnings release showed just $1.71 million of revenues, which fell short of already modest expectations. Also, the revenue growth rate of 25% year over year (YOY) is quite unimpressive for a firm with such a small starting base of operations.

Perhaps quantum annealing will eventually take off. However, D-Waves current $175 million market capitalization seems awfully steep given the minimal revenues and fairly unproven nature of the technology.

On the date of publication, Ian Bezek did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

Ian Bezek has written more than 1,000 articles for InvestorPlace.com and Seeking Alpha. He also worked as a Junior Analyst for Kerrisdale Capital, a $300 million New York City-based hedge fund. You can reach him on Twitter at @irbezek.

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Why These 3 Stocks Are the Worst Ways to Play Quantum ... - InvestorPlace

Longstanding challenges to the power grid and other critical infrastructure might begin to be resolved more quickly through quantum computing with the aid of LSU researchers, thanks to a $500,000 grant from the National Science Foundation.

LSU electrical and computer engineering and physics researchers will use the award to develop computer algorithms targeting issues that historically have been difficult to solve efficiently, such as optimizing power flow, planning for transmission network expansion, diagnosing faults, and ensuring grid resilience.

The research involving new developments in the field of quantum computing will enable multiple potential solutions being worked on at the same time, leading to an exponential speedup in solving complex optimization problems, according to the announcement..

We can achieve faster responses to changing energy demands; reduce operational costs; integrate renewable energy sources more effectively; and ultimately create a more stable, cost-efficient, and environmentally-friendly energy system, benefiting society as a whole, says Amin Kargarian, associate professor of electrical and computer engineering.

Read more.

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LSU researchers awarded NSF funding to address infrastructure ... - 10/12 Industry Report

Experimental results from a quantum computer (left) that match well with theory (right) are the first quantum-based method to show a quantum effect in the way light-absorbing molecules interact with incoming photons. Credit: Jacob Whitlow, Duke University

A quantum computer slowed simulated molecular quantum effects by a billion times, allowing researchers to directly measure them for the first time.

Researchers at Duke University have implemented a quantum-based method to observe a quantum effect in the way light-absorbing molecules interact with incoming photons. Known as a conical intersection, the effect puts limitations on the paths molecules can take to change between different configurations.

The observation method makes use of a quantum simulator, developed from research in quantum computing, and addresses a long-standing, fundamental question in chemistry critical to processes such as photosynthesis, vision, and photocatalysis. It is also an example of how advances in quantum computing are being used to investigate fundamental science.

The results were published on August 28 in the journal Nature Chemistry.

As soon as quantum chemists ran into these conical intersection phenomena, the mathematical theory said that there were certain molecular arrangements that could not be reached from one to the other, said Kenneth Brown, the Michael J. Fitzpatrick Distinguished Professor of Engineering at Duke. That constraint, called a geometric phase, isnt impossible to measure, but nobody has been able to do it. Using a quantum simulator gave us a way to see it in its natural quantum existence.

Conical intersections can be visualized as a mountain peak touching the tip of its reflection coming from above and govern the motion of electrons between energy states. The bottom half of the conical intersection represents the energy states and physical locations of an unexcited molecule in its ground state. The top half represents the same molecule but with its electrons excited, having absorbed energy from an incoming light particle.

The molecule cant stay in the top state its electrons are out of position relative to their host atoms. To return to the more favorable lower energy state, the molecules atoms begin rearranging themselves to meet the electrons. The point where the two mountains meet the conical intersection represents an inflection point. The atoms can either fail to get to the other side by readjusting to their original state, dumping excess energy in the molecules around them in the process, or they can successfully make the switch.

Because the atoms and electrons are moving so fast, however, they exhibit quantum effects. Rather than being in any one shape at any one place on the mountain at any given time, the molecule is actually in many shapes at once. One could think of all these possible locations as being represented by a blanket wrapped around a portion of the mountainous landscape.

However, due to a mathematical quirk in the system that emerges from the underlying mathematics, called a geometric phase, certain molecular transformations cant happen. The blanket cant wrap entirely around the mountain.

If a molecule has two different paths to take to get to the same final shape, and those paths happen to surround a conical intersection, then the molecule wouldnt be able to take that shape, said Jacob Whitlow, a doctoral student working in Browns laboratory. Its an effect thats hard to gain intuition for, because geometric phase is weird even from a quantum mechanical standpoint.

Measuring this quantum effect has always been challenging because it is both short-lived, on the order of femtoseconds, and small, on the scale of atoms. And any disruption to the system will prevent its measurement. While many smaller pieces of the larger conical intersection phenomenon have been studied and measured, the geometric phase has always eluded researchers.

If conical intersections exist which they do then the geometric phase has to exist, said Brown, who also holds appointments in Duke physics and chemistry. But what does it mean to say something exists that you cant measure?

In the paper, Whitlow and coworkers used a five-ion quantum computer built by the group of Jungsang Kim, the Schiciano Family Distinguished Professor of Electrical and Computer Engineering at Duke. The quantum computer uses lasers to manipulate charged atoms in a vacuum, providing a high level of control. Whitlow and Zhubing Jia, a PhD student in Browns laboratory, also expanded the capability of the system by developing ways to physically nudge the floating ions within their electromagnetic traps.

Based on how the ions are moved and the quantum state that theyre placed in, they can fundamentally exhibit the exact same quantum mechanisms as the motion of atoms around a conical intersection. And because the quantum dynamics of the trapped ions are about a billion times slower than those of a molecule, the researchers were able to make direct measurements of the geometric phase in action.

The results look something like a two-dimensional crescent moon. As depicted in the conical intersection graph, certain configurations on one side of the cone fail to reach the other side of the cone even though there is no energy barrier. The experiment, Brown says, is an elegant example of how even todays rudimentary quantum computers can model and reveal the inner quantum workings of complex quantum systems.

The beauty of trapped ions is that they get rid of the complicated environment and make the system clean enough to make these measurements, said Brown.

An independent experiment at the University of Sydney, Australia has also observed the effects of the geometric phase using an ion trap quantum simulator. The approach differs in many technical details, but the overall observations are consistent. The Sydney work will be published in the same issue of Nature Chemistry.

Reference: Simulating Conical Intersections with Trapped Ions by Jacob Whitlow, Zhubing Jia, Ye Wang, Chao Fang, Jungsang Kim and Kenneth R. Brown, 28 August 2023, Nature Chemistry.DOI: 10.1038/s41557-023-01303-0

This work was supported by Intelligence Advanced Research Projects Activity (W911NF-16-1-0082), the National Science Foundation (Phy-1818914, OMA-2120757), the Department of Energy Office of Advanced Scientific Computing Research QSCOUT program (DE-0019449), and the Army Research Office (W911NF-18-1-0218).

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From Photons to Photosynthesis: Quantum Computer Reveals Atomic Dynamics of Light-Sensitive Molecules - SciTechDaily

29 August 2023

A research project has highlighted the potential for quantum computing to deliver significant benefits for the design and operation of radiation facilities in the nuclear, medical and space industries, as Professor Paul Smith, Jacobs ANSWERS Technical Director, explains.

Modelling radiation transport is fundamental to nuclear physics and plays a part in everything from reactor design and operation, fuel fabrication, storage, transport, decommissioning and geological disposal. Beyond nuclear power and decommissioning, it plays a vital role in nuclear medicine, the space industry, food irradiation and oil well logging.

Monte Carlo codes are the reference method for creating simulations and solvingequations to understand the way in which physical energy is transferred by the absorption, emission and scattering of electromagnetic radiation - known as radiation transport.

The codes are designed to model and understand the movement and interactions of radiation particles (such as photons, neutrons, or charged particles) as they travel through different materials and interact with various structures.

There are two main approaches to solving the equations for radiation transport. In the deterministic approach traditional numerical methods are used to solve the mathematical equations -this involves a number of approximations. The alternative Monte Carlo approach involves simulating the paths of individual particles which involves less approximation but for some applications is prohibitively slow. In such cases it is used to produce high-fidelity solutions to test the accuracy of deterministic solutions which although more approximate, can be arrived at more quickly.

The ANSWERSSoftware Service, part of Jacobs, led a project to explore the potential benefitsof quantum computing in accelerating Monte Carlo methods.

Supported by the UKs National Quantum Computing Centres SparQ programme, which supports research into new applications, this project aimed to investigate the advantages of leveraging quantum computing instead of conventional digital computing to improve the runtime of Monte Carlo methods, making them more competitive.

ANSWERS provides and supports the MCBEND and MONK3D Monte Carlo codes which are widely used worldwide for radiation shielding, dose assessments, nuclear criticality safety and reactor physics analysis. For example, ANSWERS software is used to support the design and safety case production for transport flasks for radioactive materials.

Several processes contribute significantly to the computational cost of performing Monte Carlo radiation transport calculations including random number generation, nuclear database searches, ray tracing and the Monte Carlo process itself. Quantum algorithms are available or under development for each of these processes. Quantum random number generation has the clear advantage of generating truly random numbers, based on truly random quantum processes, whereas traditional computational methods are only capable of generating pseudo random numbers or quasi random numbers which can be subject to subtle correlations that can introduce bias into calculation results.

Whereas digital computers work with bits of data that are either 0 or 1, quantum computers work with qubits two-state quantum-mechanical systems that can be in a superposition of the 0 and 1 states. For example light may be horizontally or vertically polarised (try looking at an LED television through glasses with polarised lenses and tilting your head at different angles). If an individual photon of light is polarised at 45 degrees to horizontal it may be thought of as being in a superposition of the horizontal and vertical states.

This allows quantum computers to process many states in a single operation, increasing their processing power exponentially and achieving complex problem-solving which is impossible on digital computers. In practice, many quantum algorithms offer a quadratic advantage over traditional digital computers -for example, a quantum algorithm may achieve in 1000 operations what would take a million operations using a traditional algorithm.

There are certain scenarios where digital computing surpasses quantum computing. For instance, due to the specific ordering of nuclear databases (from lowest energy to highest energy), binary searches offer an exponential advantage over the quantum Grover search algorithm.

One of the biggest challenges faced by quantum computing at present is the presence of quantum noise. Being microscopic, quantum systems are very delicate.

Any interaction with the surrounding environment can change the state of the system, for example changing a qubit from state 0 to state 1 or vice versa. Random interactions with the qubits effectively add an element of noise to the answers obtained from a quantum computer. The project used Lucy, the Oxford Quantum Circuits computer, and was successful in demonstrating the effectiveness of new techniques for the reduction of quantum noise. This is currently an area of intense research activity.

The project partners - Jacobs, National Quantum Computing Centre (part of UK Research & Innovation), Oxford Quantum Circuits, National Nuclear Laboratory, Sellafield Ltd, and the University of Cambridge - note that there are promising signs that quantum algorithms could transform the computational aspects of ray tracing and Monte Carlo radiation transport simulation, but further research is needed to evaluate their applicability.

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Viewpoint: Quantum computing and the nuclear industry : Perspectives - World Nuclear News